Abstract
This paper presents the design of a semantic annotator for text and images, using standard design methodologies for both the requirements specification and the software design part (UML - Unified Modeling Language). The paper will emphasize the potential of such software and its importance in integrating the power of ontologies and semantics with annotations in a document. The association between the annotations of a document (or an image) with elements in ontology will allow not only an automatic classification of the content in it but will also enable a more exhaustive and effective search among the relationships between various documents. In addition, integrating the use of ontologies with all of their functionalities will make it possible to both have a complete and structured view of data, as well as inferred data production which will produce additional knowledge that will benefit various domains such as text analysis, intelligent search, and image processing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abromeit, F., Chiarcos, C.: Automatic detection of language and annotation model information in Conll corpora (2019)
Amardeilh, F.: Ontopop or how to annotate documents and populate ontologies from texts. In: ESWC 2006 Workshop on Mastering the Gap: From Information Extraction to Semantic Representation, vol. 187. CEUR Workshop Proceedings (2006)
Amato, A., Aversa, R., Branco, D., Venticinque, S.: Semantic wrap and personalized recommendations for digital archives. Lect. Notes Data Eng. Commun. Technol. 176, 299–308 (2023). Cited by: 0
Amato, A., Branco, D., Venticinque, S., Renda, G., Mataluna, S.: Metadata and Semantic Annotation of Digital Heritage Assets: A Case Study, pp. 516–522 (2023). Cited by: 0
Branco, D., Aversa, R., Venticinque, S.: A tool for creation of virtual exhibits presented as IIIF collections by intelligent agents. In: Barolli, L. (ed.) AINA-2023, vol. 3, pp. 241–250. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28694-0_22
Di Martino, B., et al.: A big data pipeline and machine learning for uniform semantic representation of data and documents from it systems of the Italian ministry of justice. Int. J. Grid High Perform. Comput. 14(1), 1–31 (2022)
Di Martino, B., et al.: Semantic based knowledge management in e-government document workflows: a case study for judiciary domain in road accident trials. In: Barolli, L. (ed.) CISIS 2022, pp. 435–445. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08812-4_42
Di Martino, B., et al.: A semantic-based methodology for the management of document workflows in e-government: a case study for judicial processes. Knowl. Inf. Syst. 66(7), 3959–3987 (2024). https://doi.org/10.1007/s10115-024-02077-8
Di Martino, B., Marulli, F., Graziano, M., Lupi, P.: PrettyTags: an open-source tool for easy and customizable textual multi-level semantic annotations. In: Barolli, L., Yim, K., Enokido, T. (eds.) CISIS 2021. LNNS, vol. 278, pp. 636–645. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79725-6_64
McGuinness, D.L., Van Harmelen, F., et al.: Owl web ontology language overview. W3C Recommend. 10(10), 2004 (2004)
Strippel, C., Laugwitz, L., Paasch-Colberg, S., Esau, K., Heft, A.: Brat rapid annotation tool. Medien und Kommunikationswissenschaft 70(4), 446–461 (2022)
Yimam, S.M., Biemann, C., De Castilho, R.E., Gurevych, I.: Automatic annotation suggestions and custom annotation layers in webanno. In: Proceedings of 52nd annual meeting of the Association for Computational Linguistics: System Demonstrations, pp. 91–96 (2014)
Acknowledgments
The work described in this paper has been supported by the research projects RASTA: Realtà Aumentata e Story-Telling Automatizzato per la valorizzazione di Beni Culturali ed Itinerari, Italian MUR PON Proj. ARS01 00540 and TAILOR project (https://tailor-network.eu/) funded by EU Horizon 2020 research and innovation program under Grant Agreement No. 952215.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Di Martino, B., Amato, A., Branco, D., Colucci Cante, L., Graziano, M., Venticinque, S. (2024). Towards a Semantic Annotation Software Design for Images and Texts. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-031-70011-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-70011-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-70010-1
Online ISBN: 978-3-031-70011-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)